Aspect ratio of nano/microstructures determines Staphylococcus aureus adhesion on PET and titanium surfaces

Aims: Joint infections cause premature implant failure. The avoidance of bacterial colonization of implant materials by modification of the material surface is therefore the focus of current research. In this in vitro study the complex interaction of periodic structures on PET and titanium surfaces on the adhesion of Staphylococcus aureus is analysed. Methods and Results: Using direct laser interference patterning as well as roll-to-roll hot embossing methods, structured periodic textures of different spatial distance were produced on surfaces and S. aureus were cultured for 24 h on these. The amount of adhering bacteria was quantified using fluorescence microscopy and the local adhesion behaviour was investigated using scanning electron microscopy. For PET structures, minimal bacterial adhesion was identified for an aspect ratio of about 0·02. On titanium structures, S. aureus adhesion was significantly decreased for profile heights of < 200 nm. Our results show a significantly decreased bacterial adhesion for structures with an aspect ratio range of 0·02 to 0·05. Conclusions: We show that structuring on surfaces can decrease the amount of S. aureus on titanium and PET as common implant materials. Significance and Impact of the Study: The study highlights the immense potential of applying specific structures to implant materials to prevent implant colonization with pathogen bacteria.Fil: Meinshausen, A. K.. Otto-von-Guericke-Universität Magdeburg; AlemaniaFil: Herbster, M.. Otto-von-Guericke-Universität Magdeburg; AlemaniaFil: Zwahr, C.. Technische Universität Dresden; AlemaniaFil: Soldera, Marcos Maximiliano. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Patagonia Norte. Instituto de Investigación y Desarrollo en Ingeniería de Procesos, Biotecnología y Energías Alternativas. Universidad Nacional del Comahue. Instituto de Investigación y Desarrollo en Ingeniería de Procesos, Biotecnología y Energías Alternativas; ArgentinaFil: Müller, A.. Otto-von-Guericke-Universität Magdeburg; AlemaniaFil: Halle, T.. Otto-von-Guericke-Universität Magdeburg; AlemaniaFil: Lasagni, A. F.. Technische Universität Dresden; AlemaniaFil: Bertrand, J.. Otto-von-Guericke-Universität Magdeburg; Alemani

Beyond brain reading: randomized sparsity and clustering to simultaneously predict and identify

International audienceThe prediction of behavioral covariates from functional MRI (fMRI) is known as brain reading. From a statistical standpoint, this challenge is a supervised learning task. The ability to predict cognitive states from new data gives a model selection criterion: prediction accu- racy. While a good prediction score implies that some of the voxels used by the classifier are relevant, one cannot state that these voxels form the brain regions involved in the cognitive task. The best predictive model may have selected by chance non-informative regions, and neglected rele- vant regions that provide duplicate information. In this contribution, we address the support identification problem. The proposed approach relies on randomization techniques which have been proved to be consistent for support recovery. To account for the spatial correlations between voxels, our approach makes use of a spatially constrained hierarchical clustering algorithm. Results are provided on simulations and a visual experiment

Characterisation of large changes in wind power for the day-ahead market using a fuzzy logic approach

Wind power has become one of the renewable resources with a major growth in the electricity market. However, due to its inherent variability, forecasting techniques are necessary for the optimum scheduling of the electric grid, specially during ramp events. These large changes in wind power may not be captured by wind power point forecasts even with very high resolution Numerical Weather Prediction (NWP) models. In this paper, a fuzzy approach for wind power ramp characterisation is presented. The main benefit of this technique is that it avoids the binary definition of ramp event, allowing to identify changes in power out- put that can potentially turn into ramp events when the total percentage of change to be considered a ramp event is not met. To study the application of this technique, wind power forecasts were obtained and their corresponding error estimated using Genetic Programming (GP) and Quantile Regression Forests. The error distributions were incorporated into the characterisation process, which according to the results, improve significantly the ramp capture. Results are presented using colour maps, which provide a useful way to interpret the characteristics of the ramp events

Linking sea level rise and socioeconomic indicators under the Shared Socioeconomic Pathways

In order to assess future sea level rise and its societal impacts, we need to study climate change pathways combined with different scenarios of socioeconomic development. Here, we present sea level rise (SLR) projections for the Shared Socioeconomic Pathway (SSP) storylines and different year-2100 radiative forcing targets (FTs). Future SLR is estimated with a comprehensive SLR emulator that accounts for Antarctic rapid discharge from hydrofracturing and ice cliff instability. Across all baseline scenario realizations (no dedicated climate mitigation), we find 2100 median SLR relative to 1986–2005 of 89 cm (likely range: 57–130 cm) for SSP1, 105 cm (73–150 cm) for SSP2, 105 cm (75–147 cm) for SSP3, 93 cm (63–133 cm) for SSP4, and 132 cm (95–189 cm) for SSP5. The 2100 sea level responses for combined SSP-FT scenarios are dominated by the mitigation targets and yield median estimates of 52 cm (34–75 cm) for FT 2.6 Wm−2, 62 cm (40–96 cm) for FT 3.4 Wm−2, 75 cm (47–113 cm) for FT 4.5 Wm−2, and 91 cm (61–132 cm) for FT 6.0 Wm−2. Average 2081–2100 annual SLR rates are 5 mm yr−1 and 19 mm yr−1 for FT 2.6 Wm−2 and the baseline scenarios, respectively. Our model setup allows linking scenario-specific emission and socioeconomic indicators to projected SLR. We find that 2100 median SSP SLR projections could be limited to around 50 cm if 2050 cumulative CO2 emissions since pre-industrial stay below 850 GtC, with a global coal phase-out nearly completed by that time. For SSP mitigation scenarios, a 2050 carbon price of 100 US$2005 tCO2 −1 would correspond to a median 2100 SLR of around 65 cm. Our results confirm that rapid and early emission reductions are essential for limiting 2100 SLR

The influence of feature selection methods on accuracy, stability and interpretability of molecular signatures

Motivation: Biomarker discovery from high-dimensional data is a crucial problem with enormous applications in biology and medicine. It is also extremely challenging from a statistical viewpoint, but surprisingly few studies have investigated the relative strengths and weaknesses of the plethora of existing feature selection methods. Methods: We compare 32 feature selection methods on 4 public gene expression datasets for breast cancer prognosis, in terms of predictive performance, stability and functional interpretability of the signatures they produce. Results: We observe that the feature selection method has a significant influence on the accuracy, stability and interpretability of signatures. Simple filter methods generally outperform more complex embedded or wrapper methods, and ensemble feature selection has generally no positive effect. Overall a simple Student's t-test seems to provide the best results. Availability: Code and data are publicly available at http://cbio.ensmp.fr/~ahaury/

Prediction intervals for future BMI values of individual children - a non-parametric approach by quantile boosting

Background: The construction of prediction intervals (PIs) for future body mass index (BMI) values of individual children based on a recent German birth cohort study with n = 2007 children is problematic for standard parametric approaches, as the BMI distribution in childhood is typically skewed depending on age. Methods: We avoid distributional assumptions by directly modelling the borders of PIs by additive quantile regression, estimated by boosting. We point out the concept of conditional coverage to prove the accuracy of PIs. As conditional coverage can hardly be evaluated in practical applications, we conduct a simulation study before fitting child- and covariate-specific PIs for future BMI values and BMI patterns for the present data. Results: The results of our simulation study suggest that PIs fitted by quantile boosting cover future observations with the predefined coverage probability and outperform the benchmark approach. For the prediction of future BMI values, quantile boosting automatically selects informative covariates and adapts to the age-specific skewness of the BMI distribution. The lengths of the estimated PIs are child-specific and increase, as expected, with the age of the child. Conclusions: Quantile boosting is a promising approach to construct PIs with correct conditional coverage in a non-parametric way. It is in particular suitable for the prediction of BMI patterns depending on covariates, since it provides an interpretable predictor structure, inherent variable selection properties and can even account for longitudinal data structures

Identification of functional rare variants in genome-wide association studies using stability selection based on random collapsing

Genome-wide association studies are a powerful approach used to identify common variants for complex disease. However, the traditional genome-wide association methods may not be optimal when they are applied to rare variants because of the rare variants’ low frequencies and weak signals. To alleviate the difficulty, investigators have proposed many methods that collapse rare variants. In this paper, we propose a novel ranking method, which we call stability selection based on random collapsing, to rank the candidate rare variants. We use the simulated mini-exome data sets of unrelated individuals from Genetic Analysis Workshop 17 for the analysis. The numerical results suggest that the selection based on a random collapsing method is promising for identifying functional rare variants in genome-wide association studies. Further research to examine the error control property of the proposed method is underway

Discovering study-specific gene regulatory networks

This article has been made available through the Brunel Open Access Publishing Fund.Microarrays are commonly used in biology because of their ability to simultaneously measure thousands of genes under different conditions. Due to their structure, typically containing a high amount of variables but far fewer samples, scalable network analysis techniques are often employed. In particular, consensus approaches have been recently used that combine multiple microarray studies in order to find networks that are more robust. The purpose of this paper, however, is to combine multiple microarray studies to automatically identify subnetworks that are distinctive to specific experimental conditions rather than common to them all. To better understand key regulatory mechanisms and how they change under different conditions, we derive unique networks from multiple independent networks built using glasso which goes beyond standard correlations. This involves calculating cluster prediction accuracies to detect the most predictive genes for a specific set of conditions. We differentiate between accuracies calculated using cross-validation within a selected cluster of studies (the intra prediction accuracy) and those calculated on a set of independent studies belonging to different study clusters (inter prediction accuracy). Finally, we compare our method's results to related state-of-the art techniques. We explore how the proposed pipeline performs on both synthetic data and real data (wheat and Fusarium). Our results show that subnetworks can be identified reliably that are specific to subsets of studies and that these networks reflect key mechanisms that are fundamental to the experimental conditions in each of those subsets